ADVANCES IN INFORMATION SCIENCE
Towards a Comprehensive Model of the Cognitive
Process and Mechanisms of Individual Sensemaking
Pengyi Zhang
Department of Information Management, Peking University, 5 Yiheyuan Road, Haidian District, Beijing,
100871, China. E-mail: pengyi.zhang@pku.edu.cn
Dagobert Soergel
Department of Library and Information Studies, Graduate School of Education, University at Buffalo, 534 Baldy
Hall, Buffalo, NY 14260. E-mail: dsoergel@buffalo.edu
This review introduces a comprehensive model of the
cognitive process and mechanisms of individual sense-
making to provide a theoretical basis for:
• empirical studies that improve our understanding of the cog-
nitive process and mechanisms of sensemaking and integra-
tion of results of such studies;
• education in critical thinking and sensemaking skills;
• the design of sensemaking assistant tools that support and
guide users.
The paper reviews and extends existing sensemaking
models with ideas from learning and cognition. It
reviews literature on sensemaking models in human-
computer interaction (HCI), cognitive system engineer-
ing, organizational communication, and library and
information sciences (LIS), learning theories, cognitive
psychology, and task-based information seeking. The
model resulting from this synthesis moves to a stronger
basis for explaining sensemaking behaviors and con-
ceptual changes. The model illustrates the iterative pro-
cesses of sensemaking, extends existing models that
focus on activities by integrating cognitive mechanisms
and the creation of instantiated structure elements of
knowledge, and different types of conceptual change to
show a complete picture of the cognitive processes of
sensemaking. The processes and cognitive mecha-
nisms identified provide better foundations for knowl-
edge creation, organization, and sharing practices and a
stronger basis for design of sensemaking assistant
systems and tools.
Introduction: Overview and Scope
Information science research and practice, including
librarianship, has made much progress in understanding
information behavior, especially information seeking, and in
helping users to find and access information. Information
retrieval has seen large advances, even though much still
needs to be done to assist users with conducting meaningful
searches; information and communication technologies
provide access to huge amounts of information. Likewise,
information-literacy education has focused on information-
search and evaluation skills. But use of the information found
and assisting users with such use has received less attention
from system builders and information-literacy educators.The
next frontier is building systems that assist users with making
sense of all the information found and to educate students and
users generally in the best ways of using and applying infor-
mation, with or without the use of such next-generation
systems. We need to bring together concepts and techniques
from information science (broadly construed to include rel-
evant areas of computer science), information design and
visualization, education, instructional design, and learning
theory to build systems and services that assist users with
and prepare them for learning and sensemaking in an
information-rich world (Neuman, 2011a,b).
As a contribution to this effort, this review introduces a
comprehensive model of the cognitive process and mecha-
nisms of individual sensemaking to improve our understand-
ing of sensemaking and to provide a theoretical basis for:
• Empirical studies of the cognitive process of sensemaking and
integration of the results of such studies
• Education in critical thinking and sensemaking skills
Received September 5, 2012; revised August 21 2013; accepted August 23,
2013
© 2014ASIS&T • Published online 23April 2014 in Wiley Online Library
(wileyonlinelibrary.com). DOI: 10.1002/asi.23125
JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY, 65(9):1733–1756, 2014
• The design of sensemaking assistant tools that support and
guide users
Sensemaking is the information task of creating an under-
standing of a concept, knowledge area, situation, problem,
or work task (application task; for definitions of information
task and work task, see Ingwersen & Järvelin, 2005;
Byström & Hansen, 2005) often to inform action. Sense-
making is a prerequisite for many work tasks such as
problem solving, decision making, planning, and executing
a plan. Sensemaking has also been defined as:
A process of forming and working with meaningful represen-
tations in order to act in an informed manner (Stefik et al., 1999;
Pirolli & Russell, 2011).
and as:
Reading into a situation patterns of significant meaning
(generalized from the original quote: individuals “realize their
reality, by reading into their situation patterns of significant
meaning; Morgan, Frost, & Pondy, 1983, p. 24”).
An important part of sensemaking involves making clear
the interrelated concepts and their relationships in a problem
or task space.
As an example, consider a business analyst who is devel-
oping a marketing plan for a product, including a TV ad
campaign and advertisements on other media such as print,
radio, and the web. To understand the whole picture, she/he
needs to gather information about the product and its competi-
tors; conduct thorough research in trade and business presses
and periodicals about the product, the company, and its com-
petitors, potential users, and market shares and trends; and
then develop multiple ideas for the marketing plan based on
the research. The analyst is involved in a sensemaking task.
Sensemaking (Brenda Dervin, the originator of the sense-
making methodology, prefers the spelling with a hyphen
while the community in computer science and more techni-
cal people in information science [for example, SIG-CHI]
use sensemaking without a hyphen) is a series of continuing
gap-defining and gap-bridging activities between situations
(Dervin, 1992, 1998). The sensemaker accomplishes each
sensemaking “moment” by defining and dealing with the
situation, the gap, and the bridge.
Sensemaking may be carried out by an individual or in a
group or organization or it may be a societal endeavor. It
involves both cognition and affect and interpersonal relation-
ships and group/organizational dynamics. Dervin and
Naumer (2010, p. 4696) distinguish work on sensemaking in
four fields: “Human Computer Interaction (HCI) (Russell’s
sensemaking); Cognitive Systems Engineering (Klein’s sen-
semaking); Organizational Communication (Weick’s sense-
making; Kurtz and Snowden’s sense-making); and Library
and Information Science (Dervin’s sense-making).” As can
be seen from Figure 1, sensemaking by individuals or groups
takes place in a rich context and has many influences. This
paper focuses on sensemaking by individuals and within
that on cognitive processes, drawing on literature from
human-computer interaction and cognitive systems engineer-
ing. Further, the paper addresses primarily conscious, delib-
erate,
and
often
somewhat
systematic
processes
of
sensemaking that usually involve deliberate search for and
use of information; but less conscious, experiential sense-
making falls within the general parameters of the sensemak-
ing models presented. Parts of the model may apply to
components of organizational sensemaking as well. In the
remainder of the paper, sensemaking is used to refer to this
limited scope.
There are several areas and literatures that overlap with
sensemaking and that present models that are similar to the
sensemaking models we discuss later in this paper: Informa-
tion seeking and learning (we include a few representative
models of both), research methods (which we have treated as
kind of sensemaking and included in our review of models),
creativity (Kaufman & Sternberg, 2010), problem solving
(the classic Polya, 1945/1957/2004, Problem solving, n.d.,
and, as an example, one of many how-to books, Treffinger,
Isaksen, & Stead-Dorval, 2005), and the many books on
writing scholarly papers, theses, etc. Including models from
these areas would have enriched our analysis of sensemaking
and related activities (see Table 1 and the SupportingAppen-
dix), but is well beyond the scope of this paper.
In the literature and in this paper, sensemaking has both a
broad and a narrow meaning. Sensemaking, broadly defined,
refers to the overall process of the following two subprocesses:
1. Seeking information, followed by extracting and filtering
the information found; also called sensing.
2. Sensemaking, narrowly defined, which refers to itera-
tively creating and updating an understanding of the situ-
ation, especially connections (for example, between
people, places, events, intentions, etc.), creating a repre-
sentation
that
can
support
decisions
or
effective
action (Klein, Moon, & Hoffman, 2006a). This can be
FIG. 1.
The Sense-making metaphor (copyright, Brenda Dervin 2010 as
published in Dervin & Foreman-Wernet, 2012, Figure 1, p. 156). Reprinted
with permission.
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DOI: 10.1002/asi
TABLE 1.
Elements of sensemaking.
Sensemaking is an iterative process that can include many activities in different sequences. They may iterate in several rounds until the goal is reached, intertwine with and
influence each other, or spiral up to the final presentation. The activities are often defined by a combination of several characteristics. Different sensemaking models
describe these activities at different levels of granularity. This figure presents a (faceted) classification of the activities and characteristics found in the models we reviewed.
0 General control and command activities applied at individual steps throughout the process
0.1 Planning − .2 Defining − .3 Evaluating − .4 Interacting − .5 Feedback − .6 Monitoring − .7 Reflecting
A Task analysis and determination of information gaps
Primarily for work tasks but also meta information tasks and learning tasks
A0 Selecting a topic (for example, for an assignment or for research)
A1 Preparing. Becoming familiar with the general or specific area of the work task / problem
A2 Task / problem definition (could be considered part of developing the structure)
A3 Identifying audience − A4 Establishing evaluation criteria for the research product and process.
B Information Seeking. Searching for information / data / structure
B0 Planning and carrying out the search process
B0.1 Determining information needs − .2 Search strategy − 2.1 Conceptual query formulation − .2.2 Selecting and sequencing sources. − 2.3 Source-specific query
formulations − .3 Executing search strategy − .4 Reviewing results − .5 Editing results. − .6 Checking helpfulness of the results
B1 Searching for data versus Searching for structure
B1.1 Searching for data
B1.1.2 Using structure to find data − .1 Searching for data guided by structure −.2 Inferring data
B1.2 Searching for structure (as contrasted with building structure) = C1.1.1.3
B1.2.1 Searching for structure based on problem / gap definition
B1.2.2 Search or activation of structure prompted by data
B2 Scanning the environment versus specific search
B3 Exploratory (pre-focus) versus focused search
B4 Searching for sources versus extracting data or structure from sources
B5 Searching in outside sources versus search in a data store or representation created for the sensemaking problem at hand
B6 Evaluating and selecting information. Judging quality and relevance NT B0.4, B0.5
C Making sense of the information / data: Analyzing and synthesizing the data. Creating a representation that fits the data into a structure or schema
At the heart of sense making is the interplay of structure and data.
C1–C3 approximate an umbrella process sequence.
C4–C8 give many characteristics of activities in the interplay of data and structure arranged into a faceted classification
C1–C3 Approximate an umbrella process sequence
C1 Building / acquiring and instantiating structure
C1.1 Building or acquiring structure or structure modification
C1.1.1 Mode of building or acquiring structure or structure modification
C1.1.1.1 Building new structure or modifying structure
C1.1.1.1 : C1.1.2.1
Building structure inductively from the data
C1.1.1.2 Activating existing structure that is close at hand (in memory, external store)
C1.1.1.3 Finding structure through search in outside sources = B1.2
C1.1.2 Basis for building or acquiring structure (combine with C1.1.1)
C1.1.2.1 Building or acquiring structure from data or prompted by data
C1.1.2.2 Building or acquiring structure from problem definition
C1.1.2.3 Building or acquiring structure from theory
C1.1.2.4 Building or modifying structure influenced by predisposition and purposes
C1.1.2.5 Building or modifying structure influenced by past experience
C1.1.3 Comparing to other structure
C1.1.4 Format of resulting structure (for example, a list of hypotheses)
C1.2 Instantiating structure / making data and structure fit. Organizing information
C1.2.2 Fitting data into the structure − .2 Discarding data −.3 Using structure to filter data
C1.3 Stage of Building / acquiring / instantiating structure or structure modification
C1.3.1 initially versus.2 Modifying, revising, replacing structure = C2.2
C2 Examining and revising structure. Determining whether completed RT C3.2
Strictly speaking, all part of C1 (in iteration); broken out for easier correspondence to other models
C2.1 Examining and questioning data and structure
C2.1.1 Examining data not yet fitted. leftover non-fitting data (residue) versus new data
C2.1.2 Tracking anomalies − .3 Detecting inconsistencies. − .4 Judging plausibility
C2.1.5 Gauging data quality (may be informed by structure)
C2.2 Modifying, revising, replacing structure
C2.2.1 To fit residue of non- or ill-fitting data, “representational shift” −.2 To fit new data
C3 Formulating the result in a report/presentation for a specific audience
May include recommendations for action, already starting D
The sense-making process may continue in this step as problems surface in the writing
This could also be a “report to self”, as an internal or external representation
C3.1 Preparing draft report/presentation
C3.2 Reflecting on the process so far and the resulting product
C3.3 Revising / refining report/presentation as needed
C3.4 Disseminating report/presentation
C4–C8 Further characteristics of activities in the interplay of data and structure
C4 Mechanisms for conceptual change. Sensemaking mechanisms
Data-driven or bottom-up versus structure-driven or top-down (Section Sensemaking Mechanisms)
C5 Degree of structure change when fitting data into structure (Section Types of Conceptual Change)
C6 Internal versus external representation
C7 Granularity of iterations by amount of information processed
D Consuming the instantiated structure. Applying the results to the work task: making a decision, executing an action, etc., by the sensemaker or the reader/listener
of a presentation
E Feedback
E1 Evaluative feedback. Did the report/presentation provide guidance for action? Action successful?
E2 Requirement feedback: Additions or changes to requirements
E3 Data feedback: New or corrected data found in application
E4 Structure feedback: Changes in the structure discerned in application
F Reflecting on the process and its results. Considering lessons learned. Updating individual and group store of knowledge, internal and/or external
JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY—September 2014
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accomplished by relating information found to previous
knowledge, creating structures, and fitting data into struc-
tures to create representations, thus arriving at an under-
standing of a situation or phenomenon (Russell, Stefik,
Pirolli, & Card, 1993). Also called making sense.
Pirolli and Card’s (2005) model shows this broad and
narrow use very clearly (in the discussion of models,
Figure 5). The outer loop encompassing all activities is
labeled “Sense-Making Loop for the Analyst”; it includes an
inner loop which contains the activities for the narrow
meaning and is called “Sense-Making Loop.” It is generally
clear from the context which meaning is intended.
The first steps in sensemaking, broadly defined, are deter-
mining an information gap and seeking information; sense-
making and information seeking and use are closely
intertwined in reality and commingled in terminology. Just
as sensemaking, broadly defined, includes information
seeking, so does information seeking and use include sense-
making, narrowly defined (in the use part). Thus, sensemak-
ing, broadly defined, and information seeking and use refer to
the same total process but emphasize different subprocesses.
Information seeking and use can be viewed as a continuum of
various levels of sensemaking, ranging from fitting informa-
tion directly to its need (for example, a task like catching a
plane that can be supported with a factoid answer—the depar-
ture time) to very complex sensemaking activities illustrated
at the beginning of the paper. Sensemaking is particularly
important when people are faced with new situations and
complexity in work tasks such as decision making, strategy
development, and policy making (Kurtz & Snowden, 2003).
Previous research on sensemaking, broadly defined, has
examined the information-seeking aspects extensively, while
research on the construction of the knowledge representations
has been by and large descriptive. Several important questions
about the creation and updating of the knowledge representa-
tions and about the cognitive process and mechanisms behind
such changes still go unanswered. Research in learning and
cognition and in task-based information-seeking and use
behaviors all bring useful insights to sensemaking research.
Through the development of a theoretical framework and a
comprehensive sensemaking model this paper aims at provid-
ing some insight into the unanswered questions.
The purpose of this paper is to construct a comprehensive
model of the cognitive process and mechanisms of indi-
vidual sensemaking by fitting together components from
sensemaking models, learning theories, and cognitive psy-
chology, as they discuss sensemaking mechanisms. Table 1,
itself a product of a sensemaking process, provides a listing
of all the elements from the sensemaking and learning
models discussed in the following in an integrated structure;
thus, it provides a useful framework for the description and
discussion of these models. The reader may also want to
look ahead at the complete model in Figure 17 as a context
for the discussion. The Online Appendix (www.dsoergel
.com/ZhangSoergel2013Appendix.docx) has a full version
of Table 1 (TABLE A-1) and two additional tables: Table
A-2 lists for each model the activities and gives for each
activity the combination of characteristics from this classi-
fication; Table A-3 gives for each element of the classifica-
tion the corresponding activities from all models.
We base our analysis on a selection from the sensemaking
and related literatures. We aimed to include all major sense-
making models. First we identified the interdisciplinary areas
where research on sensemaking has been done, including
HCI, cognitive system engineering, organizational commu-
nication, and library and information science (LIS). We then
examined each area and identified major sensemaking
models, whether so labeled or not, paying particular attention
to models that added new features. We cast a wider net to
include practical task areas for which sensemaking is dis-
cussed most often, for example, intelligence analysis, orga-
nizational practice, and criminal investigation, to add models
we found useful. We decided not to include sensemaking
models that did not further contribute to the formation of our
comprehensive model (as the information-seeking and learn-
ing models did), for example, Snowden’s multi-ontology
sensemaking (Snowden, 2005), Vivacqua and Garcia’s sen-
semaking state diagram (Vivacqua & Garcia, 2009), and Wu
et al.’s spatial sensemaking model (Wu, Zhang, & Cai, 2010).
As mentioned earlier, we consider conducting research and
most of learning as forms of sensemaking; we also wanted to
acknowledge the importance of information search for all
these activities. So we added representative search and learn-
ing models selected for their intellectual appeal and their
contribution to our overall model. For learning and cognition
theories, we selected theories or models relevant to either the
overall process or any components of sensemaking and useful
in explaining the cognitive process and mechanisms inside
the process and activities.
We used conceptual analysis to identify the main com-
ponents of the sensemaking process. For each sensemaking
model we identified the components (often referred to as
“processes” or “steps”) and how the components are com-
bined (often referred to as “loops,” “cycles,” or “phrases”).
Then we compared the components across all sensemaking
models, identified processes that are common to many and
sequences of processes that appear repeatedly; we also rec-
ognized variance in steps and sequences of sensemaking.
The remainder of the paper is organized as shown in
Figure 2: We first review relevant theoretical and empirical
research in many areas that contribute to our framework or
model and then describe the comprehensive model. We con-
clude with a discussion of future sensemaking research
directions. This paper is based on the first author’s disserta-
tion (P. Zhang, 2010) with appropriate updates.
Contributions From Sensemaking Models in HCI,
Cognitive System Engineering, Organizational
Communication, and Library and Information
Sciences (LIS)
Several sensemaking models have been proposed for dif-
ferent purposes, such as:
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DOI: 10.1002/asi
• To provide an analytical abstraction derived from empirical
user studies (Russell et al., 1993; Klein et al., 2006b; Qu,
2003; Qu & Furnas, 2008);
• To describe the sensemaking processes, either of particular or
generic user groups (Krizan, 1999; Pirolli & Card, 2005).
The existing models are by and large descriptive of the
activities and processes involved in individual or collabora-
tive sensemaking and do not explain the cognitive mecha-
nisms the sensemakers use (see sections Contributions
From Learning Theory and Contributions From Cognitive
Psychology).
Norman and Bobrow’s Model
This model comes from cognitive psychology but is quite
similar to models in HCI. Figure 3 shows the schemata
representation of the human information-processing system.
Physical signals in the external representation get through
the sensory system to become the data pool from which
schemata are constructed to support communication and
decision making (Norman & Bobrow, 1976).
Russell’s Sensemaking Model
Through several case studies, Russell et al. (1993)
explored the cost structure of sensemaking. They identified
four main processes in sensemaking, shown in Figure 4.
1. Search for representation (structure or schemas; genera-
tion loop): the sensemaker creates both representations
and the procedures that use them.
2. Create instances of representations (data coverage loop):
the sensemaker identifies information of interest and
encodes it in (fits it into) the representation.
3. Modify representation (representational shift loop): when
some data do not fit well (or not at all) into the represen-
tation,
the
sensemaker
modifies
the
representation
(restructuring).
4. Consume instantiated schemas: the sensemaker uses the
instantiated schemas in the task-specific information-
processing step.
The schemas provide top-down or goal-directed guid-
ance, by prescribing what to look for in the data, what
questions to ask, and how the answers are to be organized.
Structural representation plays a crucial role in all pro-
cesses. The generation loop represents the construction of a
structural representation; the data coverage loop represents
the fitting of data or evidence into the structure. When there
is a mismatch between the representation and the data, a
residue of data that do not fit remains, and the representa-
tional shift loop takes place to reconstruct the representation.
As presented, Russell et al.’s model starts with representa-
tion, that is, in the structure-driven mode. The initial
representation could also be generated from the data (data-
driven). In either case, the representation is checked against
the data and modified as needed.
Pirolli and Card: Notional Model of Sensemaking
Through cognitive task analysis (Chipman, Schraagen, &
Shalin, 2000), Pirolli and Card (2005) proposed a detailed
model of the steps in the sensemaking process, the variables
involved, and a general flow of how they interact. Their
model was developed to describe the work of intelligence
analysts where the product of sensemaking is a presentation
or report. The overall flow of sensemaking follows the path:
Information → schema → insight (hypotheses) → product
FIG. 2.
Organization of the paper.
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Cognitive task analysis (CTA) identified two loops of
activities:
• An information “foraging loop” that involves searching for
documents in external data sources and filtering them for
relevance and then “reading and extracting information into
some schema,” and
• A “sensemaking loop” that uses the schema to iteratively
“develop a mental model (conceptualization) that best fits the
evidence”: The schema that aids analysis; the development of
insight through the manipulation of this representation; and
the creation of some knowledge product (a presentation) or
direct action based on the insight.
This process starts out in a data-driven mode, but the
analyst often goes back to a previous step as shown in
Figure 5 through arrows in both directions. Figure 5 illus-
trates 10 processes and six representations (ranging from
external raw data to the final task presentation) of the sen-
semaking process for intelligence analysts. External data
sources are raw evidence, largely in textual form. The
“shoebox” is a much smaller subset of the external data that
is relevant for processing. The evidence file refers to snip-
pets extracted from items in the shoebox. Schemas are the
re-representation or organized marshaling of the information
so that it can be used more easily to draw conclusions.
Hypotheses are the tentative representation of those conclu-
sions with supporting arguments. Ultimately, there is a
representation in the sensemaker’s mind or externalized in a
report, based on which the sensemaker or (most likely in the
intelligence context) the recipient of the report can make a
decision and execute an action.
The Data/Frame Theory of Sensemaking
Klein et al.’s (2006b) data/frame theory (Figure 6) elabo-
rates on Russell’s data coverage loop and the representation
loop as the two major cycles of sensemaking activities,
including:
1. An elaboration cycle where the sensemaker elaborates
the structural representation (frame) by adding detail,
FIG. 3.
The memory schemata view of the human information-processing system (Norman & Bobrow, 1976, Figure 6.2, p. 118). Reprinted with
permission.
FIG. 4.
Russell’s sensemaking model (Russell et al., 1993, p. 271).
Reprinted with permission.
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questioning the frame and its explanations. Data that do
not fit (Russell’s “residue”) may be explained away to
preserve the frame, or it may result in
2) A reframing cycle (Russell’s “representational shift
loop”) where the sensemaker rejects the initial frame and
seeks to replace it with a better one.
Klein et al. (2006b) further pointed out that the elabora-
tion cycle corresponds to Piaget’s “assimilation,” and the
reframing cycle is similar to Piaget’s “accommodation”
(Piaget, 1936, 1976; see the section Types of Conceptual
Change).
Krizan’s Cyclical Model of the Intelligence Process
Intelligence analysis, a typical and intensive sensemaking
task, has been investigated extensively. The process of
FIG. 5.
Notional model of sensemaking loop for intelligence analysis (Pirolli & Card, 2005). Reprinted with permission. [Color figure can be viewed in
the online issue, which is available at wileyonlinelibrary.com.]
FIG. 6.
The data/frame theory of sensemaking (Klein, Moon, & Hoffman, 2006b). Reprinted with permission. [Color figure can be viewed in the online
issue, which is available at wileyonlinelibrary.com.]
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intelligence creation and use (in government or business)
follows a series of repeated and interrelated steps (Krizan,
1999). Each step adds value to the inputs and together they
create a substantially updated report. The analysis (sense-
making) processes convert “information” into “intelligence”
for planners and decision makers.
This model focuses on five critical steps, again in a data-
driven mode (Figure 7):
1. Planning/tasking: intelligence needs are assigned or pro-
vided by customers (end users) to analysts. Intelligence
needs are often complex and time-sensitive. Intelligence
analysts need to interpret the customer requirements
before the task can be processed.
2. In the collection step, analysts acquire information from
various
sources,
including
people
and
information
systems.
3. Processing is the selection of raw information based on its
plausibility, expectability, and support to intelligence
issues.
4. In the analysis step, analysts try to make sense of the
selected information and make higher-level analyses
including giving descriptions of the task domain, estab-
lishing explanations of phenomena, and interpreting
cause and effects.
5. In the production step, an intelligence report giving
“value-added actionable information” is created by syn-
thesizing the information extracted from all available
sources, including the intermediate products of previous
steps, to create a comprehensive assessment of an issue or
situation.
The intelligence report is disseminated to the customers
for action and for evaluation and feedback; a next round of
sensemaking activities may follow.
Barrett’s (2009) Sensemaking in Criminal Investigation
Criminal investigation and intelligence analysis share
similar task complexity and data incompleteness. Making
sense of terrorist or criminal actions involves generating,
elaborating, and testing alternative plausible hypotheses,
which is representative of complex sensemaking tasks.
Investigative sensemaking activities (Figure 8) involve
explaining the event behind the data collected, speculating
about other possibly related events, and inferring plausible
hypotheses that explain the situation. The sensemaker (such
as a jury or detective) tries to create a meaningful under-
standing of the evidence gathered through witnesses, exhib-
its, and arguments at trial. Through constructive mental
activity, possibly recalling earlier cases, the investigator
creates one or more stories to interpret the evidence
(Pennington & Hastie, 1991). (The stories are the structure
in this case; this is initially a data-driven process.) The
model describes the interaction of knowledge structure with
investigative evidence in the environment to inform the
generation and testing of stories/hypotheses.
This model adds a second way in which knowledge struc-
ture and data interact: The knowledge structure (activated by
investigative goals) guides data collection. Some of the data
collected may in turn activate knowledge structures already
held by the sensemaker, and these knowledge structures
added to the active set in turn guide the collection of still
other kinds of data—an iterative process. The important
points here are: (a) Data collection is not a “neutral” process;
the attention of the sensemaker is focused and guided by
knowledge structures active in the sensemaker’s mind; (b)
When data do not fit the active structure(s), the sensemaker
can either change the structure (as in Russell’s and many
other models) or he/she can activate another structure he/she
already knows to accommodate the new data. Such activa-
tion may also occur when a single data item is discovered.
For example, when it is discovered that a member of a group
suspected of terrorism is a specialist in making nail bombs,
the structure for bombing at public gatherings is activated
and guides the collection of a whole different set of data. If
the sensemaker does not have the needed structures at hand
this may trigger a search for structure (see Qu & Furnas,
2008).
Among the sensemaking processes illustrated (investiga-
tion, evaluation, and conclusion), the essential part, investi-
gation of cues and knowledge via mental representations,
includes activities such as:
• Generation of structure (hypotheses) to explain data
• Evaluation of completeness and coherence of structure and
mental representation
• Evaluation of conflict within mental representation (alterna-
tive hypotheses)
FIG. 7.
The cyclical model of the intelligence process (Krizan, 1999).
Reprinted with permission. [Color figure can be viewed in the online issue,
which is available at wileyonlinelibrary.com.]
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Louis (1980): Sensemaking in Organizational Entry
When a newcomer enters an organization, he or she needs
to make sense of the new environment. Louis (1980) iden-
tified several inputs to such sensemaking, including other’s
interpretations, local interpretation schemes, and predispo-
sitions and purposes and past experiences (Figure 9). These
inputs enter the sensemaker’s interpretation process to attri-
bute meaning, which leads to responding actions and an
updated understanding. Data collection occurs in the process
of interaction with others in the organization. Sensemaking,
building a structure and understanding of how the organiza-
tion works, occurs incrementally, with small updates often
immediately guiding action in the moment.
Organizational Sensemaking
The study of organizational sensemaking has identified
the same pattern of sensemaking as an iterative process of
(a) searching and (b) sensemaking, narrowly defined (Weick,
1995; Weick, Sutcliffe, & Obstfeld, 2005; Choo, 1998,
2006). Choo (1998, 2006) frames the organization’s adapt-
ability in a dynamic environment into a twofold challenge:
sensing and making sense. Sensing means noticing poten-
tially important messages in the environment—acquiring
information about events, trends, and relationships in an
organization’s external environment in which every part is
interconnected with other parts in complex and unpredict-
able ways. Making sense means constructing meaning from
FIG. 8.
Investigative sensemaking model (Barrett, 2009, Figure 1.2, p. 24). Reprinted with permission.
JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY—September 2014
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the data about the environment that have been sensed. The
challenge for sensemaking is that there are multiple interpre-
tations. Organizational sensemaking is inherently a “fluid,
open, disorderly, social process” (Choo, 1998, p. 67).
Conducting Research as Sensemaking
Conducting research is a sensemaking process. Research-
ers start with a lack or discontinuance of knowledge, recog-
nize the gaps to be filled (research questions), and conduct
research using various methods to bridge the gaps.
Compared to sensemakers in other scenarios, researchers
undertake a more systematic approach to identifying gaps;
they explain specifically what they attempt to learn or under-
stand, explicitly articulating a research topic and one or
more research questions and subquestions (Creswell, 2003;
Maxwell, 2005). Researchers then (a) collect data (search)
and (b) analyze and interpret the data (sensemaking, nar-
rowly defined).
Often the data do not exist anywhere to be “retrieved”
(exceptions include secondary analysis, meta-analysis, and
analysis of historical documents); researchers need to collect
data in experimental or natural settings. Quantitative
and qualitative methodologies take different approaches to
making sense, answering the research questions. In general,
the quantitative approach is structure-driven (deductive, or
top-down), starting with a hypothesis, collecting experimen-
tal data (often in controlled conditions), and conducting
statistical analysis to test if the data support the hypothesis
(Kirk, 1995); however, there are also data-driven quantita-
tive approaches such as exploratory data analysis and data
mining. The qualitative approach, on the other hand, may
use a combination of deductive and inductive data analysis
(Potter, 1996). For example, a researcher may use the
grounded theory approach, “grounded” in the data (data-
driven) and developing increasingly higher-level concepts
and theoretical models (Denzin & Lincoln, 2003). During
this process new questions often emerge—data activating
structure or leading to the construction of new structure.
The final product of research is usually a presentation
describing the data and the sense made (findings and con-
clusions). Several intermediate products such as coding
schemas, case reports, and researcher notes may be pro-
duced to assist sensemaking.
Research as sensemaking should also be seen in a larger
context, not confined to individual studies but to the research
program of a person or group or research enterprise in
a subject domain. Much research is exploratory, creating
structure to guide further research.
Qu and Furnas (2008): Structural
Information-Seeking Model
A common theme of the comprehensive models dis-
cussed in this section is the interaction between structure/
frame and data. Qu and Furnas (2008) focus on a
FIG. 9.
Inputs of sensemaking in organizational entry (Louis, 1980). Reprinted with permission.
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subprocess, searching for structures. They highlight the need
for finding structure as an information need in its own right
when the representation is inadequate, incomplete, or ill-
formed and the need for changing, growing, or validating
structures arises or when a piece of data “calls for” a struc-
ture into which it fits. This is related to the observation by
Barrett (2009) that pieces of data may activate structures,
except that they think of structures that the sensemaker
already has at hand while Qu and Furnas (2008) talk about
new structures to be acquired. Qu and Furnas analyzed and
developed a model for the process of seeking structures
(Figure 10).
Information-Search and Use Models and Learning Models
In this brief section, we present, by way of comparison,
representative models of information search and use and
models of learning. Together they roughly describe the same
overall process as defined in the broad meaning of sense-
making from a different perspective.
Soergel’s (1985) search process model (Figure 11) is a
very general normative model. Kuhlthau’s (1991, 1993,
2004) information-search process (ISP) model (Figure 12)
was developed primarily in the context of students’ search-
ing for information needed for school assignments; it
identifies the affective, cognitive, and physical behaviors.
Nesset’s (2013) preparing, searching, and using (PSU)
model (Figure 13) captures the information-related activities
in the conduct of an instructional unit in a school. It
identifies positive and negative feelings that accompany the
activities in each stage. It adds the preparation stage, which
is useful in general, not just for students. Neuman’s
(2011a,b)
I-LEARN
model
(Figure 14)
describes
the
process of learning with information. It is a normative model
that draws on information-seeking behavior models and
theories
of
learning/instructional
design. The Alberta
Inquiry Learning Model (Alberta Learning. Learning and
Teaching Resources Branch, 2004; Figure 15) was devel-
oped as a guide for learning and instruction.
Summary
Sensemaking is an individual or collective construction
of knowledge. The models discussed in this section all
attempt to describe the process whereby knowledge is
created. Despite the differences, the basic pattern of iterative
interaction between search for data and creating a structure
that makes sense of the data (see Figure 17) is in common to
all models. They differ in detail, in what aspects they empha-
size in defining subprocesses, and often in the sequence of
steps. Table 2 compares different sensemaking models and
illustrates the common processes.
Several important points are raised in the sensemaking
literature:
Sensemaking is comprised of iterative information
seeking (the acquisition of data, sensing) and sensemaking
(the creation of a knowledge structure, making sense)
processes. For the information-foraging/seeking loop, the
evolution of a user’s interests depends upon the changing
characteristics of the information context. New information
gives users new ideas and directions to follow. Users use
FIG. 10.
Structural information seeking (Qu & Furnas, 2008). Reprinted with permission. [Color figure can be viewed in the online issue, which is available
at wileyonlinelibrary.com.]
FIG. 11.
General search process model (Soergel, 1985, p. 343). Reprinted
with permission.
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information from the current situation to decide where to go
next (Bates, 1989; Ingwersen & Järvelin, 2005). Researchers
have identified the important role of exploratory search and
developed systems to support it (Baldonado & Winograd,
1997; Qu, 2003). The sensemaking loop, on the other hand,
including activities such as summarizing, organizing, and
identifying patterns, is not as well supported. A key task in
sensemaking is to identify patterns of concepts and relation-
ships to build on.
Data and structure play different roles in creating knowl-
edge representations. Russell et al.’s model (1993) illus-
trates that the major cost of sensemaking is related to the
structural representation, including the cost of finding or
building a representation to support required operations in
the target task, and the cost of instantiating the representa-
tions. Research indicates a separation of data and structure
in sensemaking tasks and the use of external representations
for tasks where information processing is complex (J.
Zhang, 1997, 2000).
Data and structure are intertwined. The sensemaker may
select and activate structures based on the knowledge
required for the work task, either from the sensemaker’s
memory or through search for structure. Active structures
may guide the acquisition of data. Or, the sensemaker
acquires data largely unguided. Data may lead to structure in
two ways: A piece or configuration of data may activate an
existing structure—from the sensemaker’s memory or
acquired through search for structure (for example, based on
cue extraction, Seligman, 2006) or the sensemaker creates a
new structure that accommodates the data. Regardless of
how the structure is created and activated, the sensemaker
tries to fit data into active structures (instantiate the
FIG. 12.
Model of the information-search process (Kuhlthau, 2004, p. 82). Reprinted with permission. [Color figure can be viewed in the online issue,
which is available at wileyonlinelibrary.com.]
FIG. 13.
Model of the information-search process (Nesset, 2013, p. 100). Reprinted with permission.
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structures data); put differently, the sensemaker tests the
structure against data by judging plausibility and detecting
inconsistencies (Klein et al., 2006b). Data that do not fit lead
the sensemaker to modify structures or activate additional
existing structures. In short, structure guides data acquisi-
tion, sensemakers fit data into structures, nonfitting data may
activate existing structures or lead the sensemaker to build or
update structures. Thus, sensemaking does not always have
clear beginning and ending points. The simplified waterfall
or cyclical model rarely applies; rather, there is often a
back-and-forth between several steps before moving on,
as shown through empirical evidence about several sense-
making tasks, for example, expert decision making (Klein
et al., 2006a).
To summarize, previous research has identified important
processes involved in sensemaking, involving the interre-
lated activities of the search for data and the creation of an
understanding. The importance of structure associated with
the processes is recognized: not only do structures influence
how people seek for information, they are critical to the
creation of an understanding. It seems quite clear that sen-
semakers seek structures in sensemaking tasks and their
sensemaking processes are closely related to the structural
representation of task situations. However, the sensemaking
models discussed in this section are by and large descriptive
of the activities and processes involved in individual or
collaborative sensemaking; they do not delve into the cog-
nitive process and mechanisms that contribute to the cre-
ation, modification, and update of structures when existing
external structures are not available or ready to use. They do
not address questions such as:
• What types of conceptual changes occur during the sensemak-
ing process and how do they occur?
• What mechanisms trigger the changes and enable the assimi-
lation of new information and the creation of a structural
representation?
• What are the cognitive processes and structures by which
knowledge is created and stored during the sensemaking
process?
This paper aims to enhance existing sensemaking
theories by incorporating ideas from learning and cognition
to gain a more in-depth understanding of sensemaking.
Contributions From Learning Theory
Learning is more than the collection of inputs and the
production of outputs. The mind has the ability to extract,
analyze, synthesize, and formulate received information
and stimuli and then to produce abstractions and represen-
tations that go beyond what can be directly attributed to the
input given (Gredler, 2008). Much learning is sensemaking,
especially using recorded information or systematic discov-
ery to learn concepts, ideas, theories, and facts in a domain,
such as science or history. We do not deal with rote learning,
drill, and practice for mental or physical skills, or other
forms of learning where sensemaking is not central.
TABLE 2.
Elements in selected sensemaking models (the sequence of elements in the model may differ).
Dervin, 1992, 1998
Krizan, 1999
Pirolli & Card, 2005
Russell et al., 1993
Qu and Furnas, 2008
Klein, et al., 2006b
Maxwell, 2005;
Kirk, 1995
Task analysis
Gap identification
Task planning
Research goal and
questions
Search
Exploratory search
May include both gap
identification and
gap bridging
Data collection
Search for & filter
information
Determine structural
information need
Recognize a frame
Search for theoretical
framework
Focused search
• Finding sources
• Extracting data &
structure
Search for evidence
Search for
representation
Search for structures
Elaborate a frame
Data collection
Sense-making
(narrow meaning)
Fitting data into
structures
Interpretation and
analysis
Search for support
Create instances of
representations
Define, connect and
filter the data
Data analysis
Building structures
Schematize
Search for
representation
Explore / identify
extract structure
Construct and
question a frame,
reframe
Updating knowledge
Gap bridging
Build-case
Consume instantiated
schemas
Change, grow,
validate structure
Preserve a frame or
reframe
Interpretation &
conclusion
Reevaluate
hypotheses
Modify representation
Preparing
task output
Production and
dissemination
Tell story
Writing papers,
reports, or books
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Experiential learning, while not the focus of this section,
falls within the broad parameters of the learning models
presented earlier and the learning theories reviewed here. In
this section, we review learning theories that elaborate
on sensemaking and the types of conceptual changes that
occur in sensemaking. Learner and sensemaker are used
interchangeably.
Learning Theories as Theories of Sensemaking
All the learning theories that will be discussed here
revolve around the same basic idea: fitting new information
into an existing or adapted structure; they differ in emphasis
and nuance. For a summary comparison of learning theories,
see Grabowski (1996).
Assimilation
theory
(theory
of
meaningful
learning)
(Ausubel, Novak, & Hanesian, 1978).
In meaningful learn-
ing a new piece of information is assimilated to an existing
relevant aspect of the learner’s knowledge structure:
• The development of new meanings is built on prior knowl-
edge, that is, relevant concepts and relationships. Structure
plays a crucial role in meaningful learning.
• When meaningful learning occurs, relationships between con-
cepts become more explicit, more precise, and better inte-
grated with other concepts and relationships.
Rote learning and meaningful learning are on opposite
ends of a continuum (Novak, 1998). Some direct fitting of
facts into existing knowledge structure without understand-
ing its relationships may be similar to rote learning.
Schema theory (Rumelhart & Ortony, 1977; Rumelhart &
Norman, 1981a,b; R.C. Anderson, 1984).
Schema theory
posits that knowledge is stored in human memory as
schemas (interconnected concepts and relationships) that are
actively constructed by the learner and organized in a mean-
ingful way. Schema theory implies that sensemaking is
facilitated by prior general knowledge and generic concepts
(about the task and the domain). When new information is
acquired, the sensemaker needs to actively construct a
revised or entirely new knowledge structure. When new
information is ill-fitted (Russell et al., 1993; Klein et al.,
2006b), sensemakers feel internal conflict and need to filter
the data or refine the structure. When the mismatch between
data and structure or the difference between competing
structures is too great, the sensemaker may experience cog-
nitive dissonance which needs to be resolved (Cooper &
Carlsmith, 2001; Festinger, 1957).
Generative learning theory (Wittrock, 1990; Grabowski,
1996).
The learner is actively engaged in the learning
process, working to construct meaningful understanding of
information found in the environment. Much of what is
learned is grounded in the situation where learning takes
place (Lave & Wenger, 1991; J.R. Anderson, Reder, &
Simon, 1996). Two types of meaningful relations are impor-
tant for learning to occur:
• Among the parts of new information and
• Between new information and experience (prior knowledge)
Comprehension
occurs
by
formulating
connections
between concepts, rather than simply “placing” information
into memory or “transforming” information in memory.
Wittrock (1990) (p. 354, cited from Grabowski, 1996) gives
examples of the results of generative activity: “titles, head-
ings, questions, objectives, summaries, graphs, tables, and
main ideas” (with a focus on “organizational relationships
between different components of the environment”) and
“demonstrations, metaphors, analogies, examples, pictures,
applications, interpretations, paraphrases, and inferences”
FIG. 14.
I-LEARN stages and elements (Neuman, 2011b, p. 97). Reprinted with permission.
FIG. 15.
Inquiry learning model (Alberta Learning. Learning and
Teaching Resources Branch 2004, p. 10). Reprinted with permission.
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(with a focus on the “relationships between the external
stimuli and the memory components”).
Structural Knowledge Acquisition.
Perhaps the central
concept related to learning and sensemaking is structural
knowledge. Structural knowledge plays a key role in the
creation of new understanding or reframing of existing
knowledge. For example, learners who were given the task
of creating a semantic network performed significantly
better on other learning tasks such as relationship judgments
(Jonassen & Wang, 1993).
Structural knowledge supports higher-order thinking in
the form of analogical reasoning. The acquisition of struc-
tural knowledge may be a direct result of structural infor-
mation seeking (Qu & Furnas, 2008) or may be taught, or it
may be a result of generative activities described by the
learning theories discussed in this section. The construction
of personally relevant knowledge structures is the key to
individual sensemaking. Structural knowledge acquisition
improved significantly by focusing the learner’s attention on
structural aspects of the information in the environment.
Visual tools help the acquisition of structures. The best task
performance on analogy was achieved by learners who work
with visual support of a graphical browser and focus on
structural relationships (Jonassen & Wang, 1993).
Types of Conceptual Change
Learning theory postulates types of conceptual changes
in the mental representation of knowledge in learning/
sensemaking. Piaget (1936, 1976) identified two types of
cognitive/conceptual change in knowledge acquisition:
• Assimilation: the addition of information to existing knowl-
edge structures
• Accommodation: the modification or change of existing
knowledge structures
Following Piaget, the schema theorists distinguished
three ways in which existing schemas can be modified by
new experience or information (Rumelhart & Norman,
1981a,b; Vosniadou & Brewer, 1987):
• Accretion (Piaget’s assimilation): the gradual addition of
factual information within existing schemas without schema
change. Accretion may add new data when prior data are
completely missing or fill gaps when prior data are incom-
plete (Chi, 2007).
Piaget’s accommodation is refined according to the
degree of structure change:
• Tuning: Evolutionary conceptual change in the schemas for
organizing and interpreting information, or weak revision, the
modification of existing knowledge structures. These changes
may involve “generalizing or constraining the extent of a
schema’s applicability, determining its default values, or
otherwise improving the accuracy of the schema” to best fit
the data (Vosniadou & Brewer, 1987, p. 52).
• (Radical) restructuring: conceptual changes that involve the
radical change of existing structures or creation of new struc-
tures. Such radical changes often take place when prior
knowledge conflicts with new information. New structures are
constructed either to reinterpret old information or to account
for new information.
Table 3 shows a comparison of conceptual change recog-
nized in the literature.
For accretion to occur, sensemakers need to recognize
how well new information fits into an existing schema in
their knowledge. For weak revisions, the concepts them-
selves are not radically changed; they may be broadened or
their definition otherwise slightly changed. Similarly, the
nature of links may change slightly, and a few connections
may change without changing the basic structure that con-
nects the concepts. Such change can result from the concepts
having acquired more attributes, certain attributes becoming
more or less salient, and so forth. Radical changes or abrupt
changes, on the other hand, occur when new concepts and
relationships are introduced to either complement or replace
the existing concepts and relationships in the knowledge
structure.
Contributions From Cognitive Psychology
This section reviews theories in cognitive psychology
that deal with internal and external representations of
knowledge structure and with the mechanisms that trigger
conceptual changes in such structures.
Knowledge Representations
Choosing
the
right
knowledge
representation
is
extremely important for sensemaking, problem solving,
and learning. Sensemaking uses internal representations (or
mental models) and external representations (J. Zhang,
1997; Richardson & Ball, 2009), perhaps best viewed as one
TABLE 3.
Types of conceptual change.
Piaget, 1936, 1976
Chi, 2007
Rumelhart & Norman, 1981a
Vosniadou & Brewer, 1987
Assimilation
Adding new knowledge
Accretion
(deals only with structure changes)
Gap filling
Accommodation
Conceptual change
Tuning
Weak-revision
Restructuring
Radical restructuring
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integrated representation consisting of coupled internal and
external parts. Internal representations are constructed by
the sensemaker and worked on through internal mecha-
nisms. External representations are constructed by the
sensemaker or other people or computer programs or the
sensemaker working with computer programs; they are
worked on by the sensemaker or by computer programs
using external mechanisms which are often prescribed. The
processes and representations of sensemaking migrate to
wherever cost is the lowest (Kirsh, 2009). External repre-
sentations may be internalized by the sensemaker and
become part of her or his internal structures, or manipulated
without becoming part of the internal structure. Conversely,
internal representations may be elicited and stored as exter-
nal representations. Tools for externalizing internal repre-
sentations such as graphic organizers (Ausubel et al., 1978)
and tools for generating external representations automati-
cally (computer visualizations) in various application
domains are developed to support sensemaking tasks
(Gaines, 2010) A sensemaking support tool must help
sensemakers to form good representations.
Table 4 shows a classification of types of knowledge
and forms of representations (content and form are not
clearly distinguishable here), which is based primarily on
Rumelhart and Norman (1988). Multiple representational
formats may exist at the same time in the representation
system, for different pieces of knowledge or the same piece
of knowledge (Rumelhart & Norman, 1988).
Different aspects of the world may be represented
through different representational formats that map best into
the sets of operations one wishes to perform. Propositional
representations form the largest part of the knowledge struc-
tures that focus on meaning (Rumelhart & Norman, 1988).
Many theories in cognition (for example, Rumelhart &
Ortony, 1977; Carley & Palmquist, 1992; Jonassen &
Henning, 1996) and many data models and theories of text
structure agree that representations of propositional knowl-
edge can be seen as built from:
• Entities / concepts
• Relationship types, binary or n-ary relationships
• Statements/propositions connecting two or more entities with
a relationship (relationship instances)
Several statements can be connected into maps or net-
works (concept maps, semantic networks). People use map-
like structures to make sense of information (Hoffman,
1992). Schema/frame theory (Wertheimer, 1938; Minsky,
1975, 1977; Rumelhart & Ortony, 1977) views personal
knowledge as stored in schemas that comprise mental
constructs for ideas. A schema is a package of several propo-
sitions, integrated information on a topic. A schema is a
structure template (a frame, the definition of a class in
object-oriented programming). A schema/frame instance is
an instantiated structure (a frame with the slots filled, an
object belonging to a class).
Many sensemaking and problem-solving tasks, such as
comparing products (in order to choose one) or determining
a product price that will maximize profits, require more data
than most people can easily manipulate in their minds, so an
external representation suitable for the sensemaking task at
hand is needed (Faisal, Attfield, & Blandford, 2009). Exter-
nal representations can serve as memory aids to extend
working memory, form permanent archives, and allow
memory to be shared (J. Zhang, 2000; Kirsh, 2010).
There are many forms of external representation: text,
formatted text (as in hierarchical outlines), tabular arrange-
ment of text, diagrams, maps showing relationships between
entities (concept maps, semantic networks), tables and
graphs of numbers, databases, simulation models, photo-
graphs and schematic representation of various objects, just
to name a few. They can be mixed and combined in various
ways. They are suitable, to varying degrees, to present the
different types of knowledge shown in Table 4. Especially
diagrammatic and map representations support cognitive
mechanisms to recognize features more easily and make
inference directly. Spatial, argumentational, faceted, hierar-
chical, sequential, and network representations are seen in
various
sensemaking
tasks
and
sensemaking
support
systems (Ausubel et al., 1978; Faisal et al., 2009). Different
forms of representations are found useful at different stages
of sensemaking (Attfield & Blandford, 2011). There is a vast
literature on the use of external representations in data
analysis and sensemaking, for example, Tufte’s books on
visual display, especially Tufte (2006), Arnheim (1969),
books on visual thinking tools and graphic organizers
(Hyerle, 2008; Hyerle & Alper, 2011), and books on
TABLE 4.
Three-dimensional classification of types of knowledge.
1 Declarative versus procedural knowledge
1.1 Declarative: knowledge about what is (or was or could be)*
1.2 Procedural: knowledge about how to (procedures and processes; J.R. Anderson, 1976, 1983; ten Berge & van Hezewijk, 1999)*
2 Propositional versus analogical knowledge
2.1 Propositional: knowledge structure represented as a set of symbols. Most knowledge stored in long-term memory falls into this category.
Examples include symbolic logic, semantic networks, schemas, and frames*
2.2 Analogical: knowledge structure that has a direct correspondence between the external world and the internal representation, such as a mental
picture of a street view*
3 Implementation of knowledge representations
3.1 Parallel and distributed representations: knowledge structure that is distributed over a large set of units, also called neural networks*
Note. *From Rumelhart and Norman (1988).
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qualitative (e.g., Miles, Huberman, & Saldaña, 2013) and
quantitative research methods and statistical analysis.
Sensemaking Mechanisms
Searching and sensemaking processes involve cognitive
mechanisms that result in the accretion, tuning, and restruc-
turing of knowledge. Researchers in the areas of reasoning
(Toulmin,
Rieke,
&
Janik,
1979;
Arthur,
1994;
Johnson-Laird, 1999) and reading comprehension (Kavale,
1980), and learning (Vosniadou & Brewer, 1987) reported
mechanisms that are important to the understanding of infor-
mation and creation of knowledge. The mechanisms can be
envisioned as falling into two broad categories: data-driven
(inductive, bottom-up) and structure-driven (logic-driven,
top-down; akin to unsupervised and supervised machine
learning). While, in general, the mechanisms tend to belong
to one category or the other, the distinction between
data-driven and structure-driven mechanisms is not abso-
lute. Some mechanisms may be used in both ways and some
mechanisms may not belong to either category.
Data-driven (inductive) mechanisms involve recognizing
patterns from data and building on the patterns of similarity
and differences to generalize to the abstract structure of
knowledge. In complicated problems where little structured
knowledge is available, sensemakers look for patterns and
use the patterns to construct temporary internal models or
hypotheses or schemas to work with (Arthur, 1994). Primar-
ily data-driven mechanisms include:
• Key item extraction: processing text to identify key concepts
as expressed by words or phrases (Kavale, 1980).
• Schema induction: the discovery of the regularities in the
co-occurrence of certain phenomena (Rumelhart & Norman,
1981a; Vosniadou & Brewer, 1987; Riloff, 1996; Patwardhan
& Riloff, 2006).
• Generalization: making claims about groups based on a
sufficiently representative sample (Toulmin et al., 1979; Chi,
1992).
Structure-driven/logic-driven mechanisms involve using
knowledge schemas and logic to make arguments or reach
conclusions.
Primarily
structure-driven
mechanisms
include:
• Definition: defining different aspects of a concept, such as
purpose, function, and use (Kavale, 1980) or using existing
definitions.
• Specification: specifying as conditions or requirements of a
problem or task (Vosniadou & Brewer, 1987).
• Elimination: eliminating concepts that do not meet certain
criteria in certain attributes (Kavale, 1980).
• Explanation-based mechanisms, or reasoning from cause:
examining
the
causal
connections
of
two
phenomena
(Toulmin et al., 1979).
• Inference: drawing a conclusion or making a logical judgment
on the basis of available evidence and prior conclusions
(Johnson-Laird, 1999).
Mechanisms spanning both categories: Some mecha-
nisms can be used either bottom-up or top-down:
• Comparison: the comparison of facts, concepts, or relation-
ships (Kavale, 1980)
Ë Similarity: the recognition of common features or attributes
shared by concepts (Vosniadou & Ortony, 1989)
Ë Differentiation or discrimination: the recognition of differ-
ent features of concepts (Vosniadou & Brewer, 1987; Chi,
1992)
• Classification: relating a concept to a broader conceptual cat-
egory and grouping of sufficiently similar concepts (Kavale,
1980); classification is based on comparison. Classification
may be manifested through bottom-up approaches such as
clustering or unsupervised learning, or through top-down
approaches such as assigning items to pre-established catego-
ries or supervised learning.
• Analogy and metaphor: concepts from different domains
that are alike in some ways, especially in the structural
relationships they express, may share common features or
belong to a common abstract category and may exhibit other
common characteristics (Toulmin et al., 1979; Vosniadou &
Ortony, 1989; Gibbs, 2008).
• Semantic fit: examining the reasonableness with which a
concept appears to fit a certain slot as it relates to the meaning
of the knowledge structure as a whole (Kavale, 1980).
Other mechanisms that do not belong to either the data-
driven or logic-driven approach:
• Questioning: asking questions to oneself can be used to assess
knowledge levels; asking questions to others can be an effec-
tive strategy to bridge gaps in knowledge (Flavell, 1979).
• Socratic dialogue: critical dialogue to facilitate the recogni-
tion of inconsistencies in the current schema. Recognition
of anomalies can serve an important function in initiating
schema restructuring (Vosniadou & Brewer, 1987).
Research found that these mechanisms were used in
intelligence analysis (P. Zhang et al., 2008), spatial sense-
making (Wu et al., 2010), and other information-use
scenarios related to decision-making and choice-making
(Savolainen, 2009).
Contributions From Task-Based
Information-Seeking Research
While this literature does not contribute specific elements
of the model, it provides important context for sensemaking.
Sensemaking as an information task is often required for and
carried out in the context of a work task (application task;
Ingwersen, 1992; Ingwersen & Järvelin, 2005; Byström &
Hansen, 2005). Some information tasks require a “meta
information task,” such as making sense of the information
landscape before carrying out a search for information; thus,
in some cases what is said about work tasks in this section
may apply to an information task that is prepared through
a meta information task; in other words, a meta infor-
mation task is to the corresponding information task as an
JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY—September 2014
1749
DOI: 10.1002/asi
information task is to the corresponding work task. Further-
more, the distinction between information task (gathering
information, making sense of information) and work task
(applying information found or applying the sense made in
sensemaking) is somewhat fluid. There are work tasks, such
as many decision-making tasks, that consist primarily of
manipulating information.
Requirements for sensemaking come from the character-
istics of the work task. Ultimately, sensemakers or their
“customers” need to complete a work task based upon sense
made in the sensemaking task. The internal and external
representations constructed during the sensemaking process
need to fit the work task, or they must be updated (Russell
et al., 1993).
Task-based,
information-seeking
research
examined
work tasks with various complexities, such as routine
information-processing tasks that require very little case-by-
case consideration during the task performance; normal
information-processing tasks that require predictable case-
by-case consideration; and decision tasks that are most
complex and require several decisions specific to each case
(Byström & Järvelin, 1995; Vakkari, 1999; Byström, 2002;
Vakkari & Hakala, 2000). Among several work task charac-
teristics discussed in the review paper by Kim and Soergel
(2005), the work tasks that require at least some degree of
sensemaking often involve:
• New situations or problems
• Complex, less structured situations or problems
• A new domain
• An unclear information need
Findings suggest that at different stages of the work
task different types of information (domain information,
general information about how to solve a problem, and
information specific to the work task) were sought; for
example, background information, information that is rel-
evant in general terms, is sought at the beginning (pre-
focus) stage, whereas information that is more specific,
more pertinent to a chosen focus, is used at the end of the
work task (White, 1975; Kuhlthau, 1991, 2004). Sense-
making tools should provide information organization
mechanisms that are flexible enough to support different
stages of a work task.
Research in topical relevance (Huang & Soergel, 2006)
reveals different ways in which a piece of information may
be useful to a work task. For example, a piece of informa-
tion may be useful to the sensemaking task because it pro-
vides information about a similar work task or topic,
allowing the sensemaker to transfer solutions to the work
task at hand.
A Comprehensive Model of the
Cognitive Process and Mechanisms
of Individual Sensemaking
Our comprehensive information-seeking and sensemak-
ing model takes account of the iterative nature of sensemak-
ing and emphasizes the creation of instantiated structure
elements of knowledge. Building on previous sensemaking
models, a few representative information-seeking and
learning models, and theories of cognition and learning, the
model shows the cognitive processes of sensemaking and
provides explanatory power by incorporating the underlying
mechanisms and different types of conceptual change. An
earlier version of the model was presented in P. Zhang et al.
(2008).
Figure 16 shows the elements involved: processes, activi-
ties, mechanisms, and outcomes of sensemaking. These
FIG. 16.
Sensemaker’s toolbox.
1750
JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY—September 2014
DOI: 10.1002/asi
elements can be combined in many ways; the activities can
be executed in many different sequences, using different
mechanisms, and leading to any of the outcomes.
Figure 17 shows a framework in which often-observed
patterns can be easily identified but which also allows for
many different paths. The sensemaking process consists of
several iterative loops of information seeking and sensemak-
ing. The sensemaker starts with her/his existing knowledge
(or the lack of knowledge) of a problem or work task
situation and ends with an updated conceptual structure,
different parts of which may be updated through accretion,
tuning, or restructuring.
Sensemakers may start with an exploratory search and
identify gaps in the existing knowledge, or identify gaps
directly by analyzing the problem or planning the work task.
Exploratory search is the pre-focus stage of seeking for
information. During this process, sensemakers identify a
problem, realize they need more information, and, through
exploring or browsing or broad search, learn about what
information they need to update their knowledge. During
exploratory search, sensemakers may look for both data and
structure and move through the structure loop and data loop
in an embryonic form.
Focused search is a process in which sensemakers search
for information about specific aspects of the work task
situation, having specific questions in mind. The questions
represent the gaps identified through problem analysis and
through exploratory search and browsing.
The identification of gaps occurs at various stages with
different levels of specificity. At the very beginning, the
identified gap is a loose notion of lack of knowledge on
some topic or problem. As searching and sensemaking con-
tinue, more specific gaps may be identified, including data
gaps and structure gaps.
If a structure gap is identified, sensemakers may, in
varying proportions:
• Search for structures created and described by others and put
together a structure from what they found combined with
what they already know.
• Examine the relationships of various parts of the internal
structures in their existing knowledge, look for patterns in the
data, and build their own structure or structure modification.
If a data gap is identified, the sensemaker conducts
focused search looking for the particular pieces of data, and
fits the data found into the previously built structure (instan-
tiating representations). If the data needed are not available
from an existing source, the sensemaker needs to either do
without the data or conduct original data collection.
There are two mini-loops involved: the data loop and the
structure loop (depending on the focus of a particular itera-
tion of the sensemaking process), both of which are embed-
ded in a larger loop of sensemaking in which knowledge is
consistently updated. Sensemakers may take various paths,
and the loops may be closely intertwined.
FIG. 17.
A comprehensive model of the cognitive process and mechanisms of individual sensemaking.
JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY—September 2014
1751
DOI: 10.1002/asi
Information seeking/search, whether for data or for struc-
ture, can be characterized from three perspectives:
1. Scanning and monitoring the environment versus specific
search for information from the environment triggered by
problems or work tasks at hand, possibly guided by a
representation that needs to be instantiated with data, a
representation derived from analyzing the work task.
2. Pre-focus, exploratory search and browsing for structure
and data versus focused search.
3. Searching for sources versus searching in sources (infor-
mation extraction).
Instantiating structures may result in accretion (the data fit
with the existing structure), or in tuning (the sensemaker
makes minor modifications on the structure so it fits the data),
or in radical restructuring. Using structures created by others
(usually found through a search for structures) may result in
tuning (the gradual change in knowledge structure) or in
restructuring (the radical change in knowledge structure).
Different pieces of the structure may remain unchanged or
changed through accretion, tuning, or restructuring.
Some sensemakers may start top-down, create structures
and then search for data to fit in; others may start bottom-up
from the data, and any changes in the structure may be
accumulated from observing new data. Accumulated accre-
tion may result in tuning, and accumulated tuning may result
in restructuring.
Sensemaking activities use several mechanisms, each
serving different functions in the structuring of a conceptual
space. The bottom of Figure 17 gives a preliminary list com-
piled from the literature amended with findings from an
empirical user study (P. Zhang, 2010). Cognitive mecha-
nisms may be used alone or in combination. For example, a
sensemaker may use the key item extraction mechanism to
extract key entities/concepts and relationships as the basic
structure elements to build on. He or she may then use
specification to specify different aspects or requirements of
an extracted concept. Both data-driven mechanisms and
structure-driven mechanisms serve to come up with or vali-
date structure; they belong to the structure loop.
We applied this model in an empirical study of sense-
making processes (P. Zhang, 2010). The model proved
helpful in understanding sensemaking processes of journal-
ism and business students working on news writing and
business plan development assignments, respectively. The
analysis of these sensemaking processes led to many refine-
ments of the model.
The ultimate product of sensemaking is an updated
knowledge representation, which consists of instantiated
structures (or schemas). The mechanisms described earlier
influence the creation of instantiated structures and the
knowledge update. The sensemaker incorporates the rel-
evant data found in information seeking in the schema; put
differently, the final schema accounts for the relevant data.
Once the sensemaker incorporates the instantiated structures
into his/her existing knowledge and possibly produced a
report, the sensemaking is accomplished.
Limitations of the Model
The framework and model presented in Table 1 and
Figure 17 is the result of a sensemaking process carried out
by the authors working on the inputs discussed. Such theo-
retical analysis depends always on who does it; thus, our
model may be limited by our ability to interpret the input
models, clearly identify their component elements, and
arrange these elements in an integrated structure that makes
sense. This limitation could be overcome by a consensus
process among a larger group, but this would be well beyond
our means for this analysis.
There are other limitations as well. Sensemaking is often
a social process, but our model is limited to individual sen-
semaking. While affect clearly plays an important role in
sensemaking, we focus strictly on the cognitive component.
Our model is based primarily on secondary analysis, to a
much lesser extent on our own empirical work. It is limited
by the selection of input models; models we missed may
have contributed additional aspects, especially to Table 1.
The model needs to be applied in many different situations
to establish its usefulness. Perhaps a general model is
not possible, only models for specific sensemaking tasks.
Sensemaking in specific practice areas may follow certain
patterns (i.e., sequences of steps that sensemakers go
through) and the general model may not be most useful in
analyzing task-specific activities.
Conclusions and Discussion
The model proposed in this paper provides a framework
for analyzing and describing the cognitive process and
mechanisms of individual sensemaking; it focuses on the
changes to the conceptual space and the cognitive mecha-
nisms used in achieving these changes. It affords a better
understanding of sensemaking and provides a basis for:
• Empirical studies: Table 1 can be used as a scheme for coding
the elements of a sensemaking process that is observed. The
comprehensive model presented in Figure 17 can guide a
researcher in producing process diagrams to analyze user
processes and activities.
• Interpreting empirical user studies and integrating their
results using the framework established in Table 1 and
Figure 17.
• Education in critical thinking and sensemaking skills. Stu-
dents from an early age can be taught an overall approach to
sensemaking that includes the elements and subprocesses of
sensemaking most suited for the sensemaking task at hand.
They can be taught the general flow with the knowledge that
they often need to go back and forth. They can be taught
which approach (for example, top-down or bottom-up) is
most appropriate in a given situation or discover which
approach is best suited for their own cognitive style.
• The design of sensemaking assistant tools that support and
guide the users—from K–12 students to university students to
practitioners and researchers at the highest levels—in arrang-
ing data and thoughts and creating a wide range of external
representations and provide automated support for some
1752
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DOI: 10.1002/asi
functions, such as information extraction from text, visualiza-
tion of relationships between entities and of quantitative data,
automatic inference, and conversion from one representation
to another, for example, from a concept map to an outline.
The framework also sheds light on research in informa-
tion seeking and use from a perspective of the creation of
structured representations. The processes and cognitive
mechanisms identified provide better foundations for knowl-
edge creation, organization, and sharing practices. By char-
acterizing the overall process of sensemaking as iterations
of “information seeking–sensemaking” that are linked with
iterative updates of the conceptual space triggered by a set of
cognitive mechanisms, the model shows how sensemakers
move along from one knowledge state to the next, and what
requisites are needed to enable such movements.
Researchers in LIS have been studying task-based infor-
mation seeking and use, and they made a useful distinction
between information task and work task (Vakkari & Hakala,
2000; Byström, 2002). Sensemaking, as an information task,
is needed for many work tasks, such as problem solving and
decision making. The representations constructed during the
sensemaking process need to fit the task, or they must be
updated (Russell et al., 1993). In fact, examining informa-
tion use as gap-bridging under a sensemaking framework
provides
insights
to
information-behavior
research
(Savolainen, 2006). Task-based, information-seeking and
use research can benefit from the analysis of creation and
use of structured representations for tasks and problems. In
addition to the compound nature of information tasks and
work tasks, the examination of the concepts and relation-
ships in the knowledge space of users suggested that task
structure and topic structure are often intertwined and work
together to best serve the functional demands of the task.
Researchers have sometimes avoided talking about inter-
nal representations and cognitive aspects of sensemaking
because of the difficulty in assessing what is in a user’s mind
(Cacioppo & Petty, 1981; Chi, 2006; Das, Naglieri, & Kirby,
1994) and the limitations of using verbal reports and obser-
vations as data to interpret mental process (Nisbett &
Wilson, 1977; Ericsson & Simon, 1993; Hoffman, Shadbolt,
Burton, & Klein, 1995). However, the cognitive processes
and mechanisms are fundamental to information-behavior
research and need more attention in the field.
Tools have been developed to support sensemaking in
various ways, mostly to capture intermediate products of
sensemaking such as insights (Gersh, Lewis, Montemayor,
Piatko, & Turner, 2006) and analytical thoughts (Lowrance,
Harrison, & Rodriguez, 2001), and to provide a workspace
of the intermediate representations (Wang & Haake, 1997;
Hsieh
&
Shipman,
2002;
Wright,
Schroh,
Proulx,
Skaburskis, & Cort, 2006). However, there is less support
for connecting intermediate products to the conceptual
structure that users develop. A sensemaking tool should
support the ability to “flexibly arrange, re-arrange, group,
and name and re-name groups” (Hearst, 2009, p. 169) of raw
information and intermediate products of sensemaking. The
comprehensive
information-seeking
and
sensemaking
model provides the basis for developing sensemaking tools
that support users’ structuring a conceptual space using
various sources, including search results and intermediate
structured representations (which can themselves be part
of the concept space) such as concept maps, templates,
and outlines. With the growth of computer-based informa-
tion systems, computer-generated displays as external
representation can help the quality of complex information-
processing tasks for many types of tasks. Much prior
work on the role of external representations in individual
problem solving used well-structured problems. Further
studies
need
to
investigate
ill-structured,
open-ended
problems.
We need good sensemaking assistant tools to help users
learn and to make best use of large amounts of information.
To design such tools we need a better understanding of the
sensemaking process, both of what people actually do and
what they should do to achieve better results. Our compre-
hensive model of the individual cognitive processes points
toward arriving at such an understanding. The model would
be even more useful if it were augmented to consider affec-
tive, social, and organizational factors; we are interested in
working with others on such an expansion.
Acknowledgment
We were formerly at the College of Information Studies,
University of Maryland. This work was partially supported
by the Eugene Garfield Dissertation Fellowship from the
Beta Phi Mu Library & Information Studies Honor Society.
The first author also received support from the Beijing Key
Discipline Construction Fund. We thank the editor and two
anonymous reviewers for their constructive comments,
which helped us to significantly improve the article.
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Supporting Information
Additional Supporting Information may be found in the
online version of this article at wileyonlinelibrary.com:
Appendix A
classification of sensemaking activities
linked to a number of sensemaking models.
Table A-1.
Elements of Sensemaking. A faceted classi-
fication (expanded version of Table 1).
Table A-2.
Sensemaking, search, and learning models
linked to sensemaking activities classified in Table A-1.
Table A-3.
The sensemaking activities classified in
Table A-1 linked to the elements of sensemaking, search,
and learning models (a much expanded version of Table 2).
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DOI: 10.1002/asi